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公开(公告)号：US20230005486A1
公开(公告)日：2023-01-05
申请号：US17855149
申请日：2022-06-30
Applicant: Pindrop Security, Inc.
Inventor： Tianxiang Chen , Elie Khoury
Abstract: Embodiments include a computer executing voice biometric machine-learning for speaker recognition. The machine-learning architecture includes embedding extractors that extract embeddings for enrollment or for verifying inbound speakers, and embedding convertors that convert enrollment voiceprints from a first type of embedding to a second type of embedding. The embedding convertor maps the feature vector space of the first type of embedding to the feature vector space of the second type of embedding. The embedding convertor takes as input enrollment embeddings of the first type of embedding and generates as output converted enrolled embeddings that are aggregated into a converted enrolled voiceprint of the second type of embedding. To verify an inbound speaker, a second embedding extractor generates an inbound voiceprint of the second type of embedding, and scoring layers determine a similarity between the inbound voiceprint and the converted enrolled voiceprint, both of which are the second type of embedding.
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公开(公告)号：US20220392452A1
公开(公告)日：2022-12-08
申请号：US17832146
申请日：2022-06-03
Applicant: Pindrop Security, Inc.
Inventor： Payas GUPTA , Elie KHOURY , Terry NELMS, II , Vijay BALASUBRAMANIYAN
Abstract: Disclosed are systems and methods including computing-processes executing machine-learning architectures extract vectors representing disparate types of data and output predicted identities of users accessing computing services, without express identity assertions, and across multiple computing services, analyzing data from multiple modalities, for various user devices, and agnostic to architectures hosting the disparate computing service. The system invokes the identification operations of the machine-learning architecture, which extracts biometric embeddings from biometric data and context embeddings representing all or most of the types of metadata features analyzed by the system. The context embeddings help identify a subset of potentially matching identities of possible users, which limits the number of biometric-prints the system compares against an inbound biometric embedding for authentication. The types of extracted features originate from multiple modalities, including metadata from data communications, audio signals, and images. In this way, the embodiments apply a multi-modality machine-learning architecture.
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公开(公告)号：US20220337924A1
公开(公告)日：2022-10-20
申请号：US17857618
申请日：2022-07-05
Applicant: Pindrop Security, Inc.
Inventor： Nick Gaubitch , Scott Strong , John Cornwell , Hassan Kingravi , David Dewey
Abstract: Systems, methods, and computer-readable media for call classification and for training a model for call classification, an example method comprising: receiving DTMF information from a plurality of calls; determining, for each of the calls, a feature vector including statistics based on DTMF information such as DTMF residual signal comprising channel noise and additive noise; training a model for classification; comparing a new call feature vector to the model; predicting a device type and geographic location based on the comparison of the new call feature vector to the model; classifying the call as spoofed or genuine; and authenticating a call or altering an IVR call flow.
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公开(公告)号：US20220224793A1
公开(公告)日：2022-07-14
申请号：US17706398
申请日：2022-03-28
Applicant: Pindrop Security, Inc.
Inventor： Akanksha , Terry Nelms , Kailash Patil , Chirag Tailor , Khaled Lakhdhar
Abstract: Embodiments described herein provide for detecting whether an Automatic Number Identification (ANI) associated with an incoming call is a gateway, according to rules-based models and machine learning models generated by the computer using call data stored in one or more databases.
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公开(公告)号：US11355103B2
公开(公告)日：2022-06-07
申请号：US16775149
申请日：2020-01-28
Applicant: PINDROP SECURITY, INC.
Inventor： Hrishikesh Rao
IPC: G10L15/197 , G10L15/04 , G10L15/30 , G10L15/22 , G10L15/08
Abstract: Embodiments described herein provide for a computer that detects one or more keywords of interest using acoustic features, to detect or query commonalities across multiple fraud calls. Embodiments described herein may implement unsupervised keyword spotting (UKWS) or unsupervised word discovery (UWD) in order to identify commonalities across a set of calls, where both UKWS and UWD employ Gaussian Mixture Models (GMM) and one or more dynamic time-warping algorithms. A user may indicate a training exemplar or occurrence of call-specific information, referred to herein as “a named entity,” such as a person's name, an account number, account balance, or order number. The computer may perform a redaction process that computationally nullifies the import of the named entity in the modeling processes described herein.
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Patent Agency Ranking
公开(公告)号：US20220165275A1
公开(公告)日：2022-05-26
申请号：US17491312
申请日：2021-09-30
Applicant: PINDROP SECURITY, INC.
Inventor： Payas GUPTA , Terry NELMS, II
Abstract: Embodiments described herein provide for a voice biometrics system execute machine-learning architectures capable of passive, active, continuous, or static operations, or a combination thereof. Systems passively and/or continuously, in some cases in addition to actively and/or statically, enrolling speakers. The system may dynamically generate and update profiles corresponding to end-users who contact a call center. The system may determine a level of enrollment for the enrollee profiles that limits the types of functions that the user may access. The system may update the profiles as new contact events are received or based on certain temporal triggering conditions.
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公开(公告)号：US20220141334A1
公开(公告)日：2022-05-05
申请号：US17491292
申请日：2021-09-30
Applicant: PINDROP SECURITY, INC.
Inventor： Payas GUPTA , Terry NELMS, II
Abstract: Embodiments described herein provide for a voice biometrics system execute machine-learning architectures capable of passive, active, continuous, or static operations, or a combination thereof. Systems passively and/or continuously, in some cases in addition to actively and/or statically, enrolling speakers. The system may dynamically generate and update profiles corresponding to end-users who contact a call center. The system may determine a level of enrollment for the enrollee profiles that limits the types of functions that the user may access. The system may update the profiles as new contact events are received or based on certain temporal triggering conditions.
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公开(公告)号：US20220108701A1
公开(公告)日：2022-04-07
申请号：US17491363
申请日：2021-09-30
Applicant: PINDROP SECURITY, INC.
Inventor： Payas GUPTA , Terry NELMS, II
Abstract: Embodiments described herein provide for a voice biometrics system execute machine-learning architectures capable of passive, active, continuous, or static operations, or a combination thereof. Systems passively and/or continuously, in some cases in addition to actively and/or statically, enrolling speakers. The system may dynamically generate and update profiles corresponding to end-users who contact a call center. The system may determine a level of enrollment for the enrollee profiles that limits the types of functions that the user may access. The system may update the profiles as new contact events are received or based on certain temporal triggering conditions.
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公开(公告)号：US11290593B2
公开(公告)日：2022-03-29
申请号：US17317575
申请日：2021-05-11
Applicant: PINDROP SECURITY, INC.
Inventor： Akanksha , Terry Nelms, II , Kailash Patil , Chirag Tailor , Khaled Lakhdhar
Abstract: Embodiments described herein provide for detecting whether an Automatic Number Identification (ANI) associated with an incoming call is a gateway, according to rules-based models and machine learning models generated by the computer using call data stored in one or more databases.
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公开(公告)号：US11283919B2
公开(公告)日：2022-03-22
申请号：US16859370
申请日：2020-04-27
Applicant: PINDROP SECURITY, INC.
Inventor： Payas Gupta , Terry Nelms, II
Abstract: In an illustrative embodiment, a user device may block all the phone numbers used by an enterprise. When an enterprise wants to call the user, the enterprise may notify the user device through a separate secure channel that an enterprise phone number is in the process of making a phone call to the user device. The secure channel may include an authentication server that may request the user device to unblock the enterprise phone number. An incoming phone call from the enterprise phone number therefore can be trusted. After the phone call is terminated, the user device may again block the enterprise phone number. An attacker may not have access to the authentication server and a phone call from the attacker with a spoofed enterprise phone number (now blocked) may be dropped by the user device.
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